Caribbean Amphipoda (Crustacea) of Panama. Part I: parvorder Oedicerotidira.

Amphipods in the parvorder Oedicerotidira are burrowers, furrowers, or surface skimmers. Members of the parvorder share a well-developed posteroventral lobe on coxa 4, an equilobate coxa 5, an immensely elongate pereopod 7 that differs in structure from pereopod 6, and an entire telson. Within the parvorder, only the family Oedicerotidae has been documented from Bocas del Toro, Panama, represented by two species. This research documents a range extension for Hartmanodesnyei (Shoemaker, 1933) and describes a new species of Synchelidium Sars, 1892. An identification key to the species of Caribbean Oedicerotidae of Panama is provided.

The Woman Who Testified: The Pentecostalization of Psychiatry in Yaoundé, Republic of Cameroon.

This article takes a case study approach to the predominance of Pentecostalism, a Christian movement emphasizing conversion and testimony to divine grace, among patients at Sommeil Psychiatric Hospital in Yaoundé, Republic of Cameroon. I argue that certain patients' desire to serve as témoignage (French) or "testimony" (English) to life before and after Sommeil-to the efficacy of biomedical psychiatry-indicates a pattern in which patients drew on their Pentecostal affiliation to navigate psychiatric treatment. Grounded in 24 months of fieldwork with patients and families and hospital staff, I contend that patient experiences of treatment imperfectly paralleled prior and ongoing experiences of Pentecostalism, including cultivation of the desire to convert and testify. Taking this cultivation of desire as a form of subject-making, I conceptualize the entanglement of religious and therapeutic subjectivities at Sommeil as a patient-driven "Pentecostalization" of psychiatry, which offers patients plural possibilities and timeframes of health.

Composite Bloom Filters for Secure Record Linkage.

The process of record linkage seeks to integrate instances that correspond to the same entity. Record linkage has traditionally been performed through the comparison of identifying field values (e.g., Surname), however, when databases are maintained by disparate organizations, the disclosure of such information can breach the privacy of the corresponding individuals. Various private record linkage (PRL) methods have been developed to obscure such identifiers, but they vary widely in their ability to balance competing goals of accuracy, efficiency and security. The tokenization and hashing of field values into Bloom filters (BF) enables greater linkage accuracy and efficiency than other PRL methods, but the encodings may be compromised through frequency-based cryptanalysis. Our objective is to adapt a BF encoding technique to mitigate such attacks with minimal sacrifices in accuracy and efficiency. To accomplish these goals, we introduce a statistically-informed method to generate BF encodings that integrate bits from multiple fields, the frequencies of which are provably associated with a minimum number of fields. Our method enables a user-specified tradeoff between security and accuracy. We compare our encoding method with other techniques using a public dataset of voter registration records and demonstrate that the increases in security come with only minor losses to accuracy.

SOEMPI: A Secure Open Enterprise Master Patient Index Software Toolkit for Private Record Linkage.

To mitigate bias in multi-institutional research studies, healthcare organizations need to integrate patient records. However, this process must be accomplished without disclosing the identities of the corresponding patients. Various private record linkage (PRL) techniques have been proposed, but there is a lack of translation into practice because no software suite supports the entire PRL lifecycle. This paper addresses this issue with the introduction of the Secure Open Enterprise Master Patient Index (SOEMPI). We show how SOEMPI covers the PRL lifecycle, illustrate the implementation of several PRL protocols, and provide a runtime analysis for the integration of two datasets consisting of 10,000 records. While the PRL process is slower than a non-secure setting, our analysis shows the majority of processes in a PRL protocol require several seconds or less and that SOEMPI completes the process in approximately two minutes, which is a practical amount of time for integration.

A practical approach to achieve private medical record linkage in light of public resources.

OBJECTIVE: Integration of patients' records across resources enhances analytics. To address privacy concerns, emerging strategies such as Bloom filter encodings (BFEs), enable integration while obscuring identifiers. However, recent investigations demonstrate BFEs are, in theory, vulnerable to cryptanalysis when encoded identifiers are randomly selected from a public resource. This study investigates the extent to which cryptanalysis conditions hold for (1) real patient records and (2) a countermeasure that obscures the frequencies of the identifying values in encoded datasets. DESIGN: First, to investigate the strength of cryptanalysis for real patient records, we build BFEs from identifiers in an electronic medical record system and apply cryptanalysis using identifiers in a publicly available voter registry. Second, to investigate the countermeasure under ideal cryptanalysis conditions, we compose BFEs from the identifiers that are randomly selected from a public voter registry. MEASUREMENT: We utilize precision (ie, rate of correct re-identified encodings) and computation efficiency (ie, time to complete cryptanalysis) to assess the performance of cryptanalysis in BFEs before and after application of the countermeasure. RESULTS: Cryptanalysis can achieve high precision when the encoded identifiers are composed of a random sample of a public resource (ie, a voter registry). However, we also find that the attack is less efficient and may not be practical for more realistic scenarios. By contrast, the proposed countermeasure made cryptanalysis impractical in terms of precision and efficiency. CONCLUSIONS: Performance of cryptanalysis against BFEs based on patient data is significantly lower than theoretical estimates. The proposed countermeasure makes BFEs resistant to known practical attacks.

Efficient Privacy-Aware Record Integration.

The integration of information dispersed among multiple repositories is a crucial step for accurate data analysis in various domains. In support of this goal, it is critical to devise procedures for identifying similar records across distinct data sources. At the same time, to adhere to privacy regulations and policies, such procedures should protect the confidentiality of the individuals to whom the information corresponds. Various private record linkage (PRL) protocols have been proposed to achieve this goal, involving secure multi-party computation (SMC) and similarity preserving data transformation techniques. SMC methods provide secure and accurate solutions to the PRL problem, but are prohibitively expensive in practice, mainly due to excessive computational requirements. Data transformation techniques offer more practical solutions, but incur the cost of information leakage and false matches. In this paper, we introduce a novel model for practical PRL, which 1) affords controlled and limited information leakage, 2) avoids false matches resulting from data transformation. Initially, we partition the data sources into blocks to eliminate comparisons for records that are unlikely to match. Then, to identify matches, we apply an efficient SMC technique between the candidate record pairs. To enable efficiency and privacy, our model leaks a controlled amount of obfuscated data prior to the secure computations. Applied obfuscation relies on differential privacy which provides strong privacy guarantees against adversaries with arbitrary background knowledge. In addition, we illustrate the practical nature of our approach through an empirical analysis with data derived from public voter records.

Quantifying the Correctness, Computational Complexity, and Security of Privacy-Preserving String Comparators for Record Linkage.

Record linkage is the task of identifying records from disparate data sources that refer to the same entity. It is an integral component of data processing in distributed settings, where the integration of information from multiple sources can prevent duplication and enrich overall data quality, thus enabling more detailed and correct analysis. Privacy-preserving record linkage (PPRL) is a variant of the task in which data owners wish to perform linkage without revealing identifiers associated with the records. This task is desirable in various domains, including healthcare, where it may not be possible to reveal patient identity due to confidentiality requirements, and in business, where it could be disadvantageous to divulge customers' identities. To perform PPRL, it is necessary to apply string comparators that function in the privacy-preserving space. A number of privacy-preserving string comparators (PPSCs) have been proposed, but little research has compared them in the context of a real record linkage application. This paper performs a principled and comprehensive evaluation of six PPSCs in terms of three key properties: 1) correctness of record linkage predictions, 2) computational complexity, and 3) security. We utilize a real publicly-available dataset, derived from the North Carolina voter registration database, to evaluate the tradeoffs between the aforementioned properties. Among our results, we find that PPSCs that partition, encode, and compare strings yield highly accurate record linkage results. However, as a tradeoff, we observe that such PPSCs are less secure than those that map and compare strings in a reduced dimensional space.

Private medical record linkage with approximate matching.

Federal regulations require patient data to be shared for reuse in a de-identified manner. However, disparate providers often share data on overlapping populations, such that a patient's record may be duplicated or fragmented in the de-identified repository. To perform unbiased statistical analysis in a de-identified setting, it is crucial to integrate records that correspond to the same patient. Private record linkage techniques have been developed, but most methods are based on encryption and preclude the ability to determine similarity, decreasing the accuracy of record linkage. The goal of this research is to integrate a private string comparison method that uses Bloom filters to provide an approximate match, with a medical record linkage algorithm. We evaluate the approach with 100,000 patients' identifiers and demographics from the Vanderbilt University Medical Center. We demonstrate that the private approximation method achieves sensitivity that is, on average, 3% higher than previous methods.

Solvent accessible surface area approximations for rapid and accurate protein structure prediction.

The burial of hydrophobic amino acids in the protein core is a driving force in protein folding. The extent to which an amino acid interacts with the solvent and the protein core is naturally proportional to the surface area exposed to these environments. However, an accurate calculation of the solvent-accessible surface area (SASA), a geometric measure of this exposure, is numerically demanding as it is not pair-wise decomposable. Furthermore, it depends on a full-atom representation of the molecule. This manuscript introduces a series of four SASA approximations of increasing computational complexity and accuracy as well as knowledge-based environment free energy potentials based on these SASA approximations. Their ability to distinguish correctly from incorrectly folded protein models is assessed to balance speed and accuracy for protein structure prediction. We find the newly developed "Neighbor Vector" algorithm provides the most optimal balance of accurate yet rapid exposure measures.